import torch
import time
import sys
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
 
# 检查是否有命令行参数传递进来，如果有则使用传递的参数作为 model_path，否则使用默认值
if len(sys.argv) > 1:
    model_path = sys.argv[1]
else:
    model_path = "/data/models/llm/models/Qwen/Qwen2.5-VL-7B-Instruct"
# default: Load the model on the available device(s)
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
#     model_path, torch_dtype=torch.float16, device_map="auto"
# )
 
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)
 
# default processor
processor = AutoProcessor.from_pretrained(model_path)
 
# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
 
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "描述这个图片"},
        ],
    }
]
 
# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to(model.device)
 
# Inference: Generation of the output
for i in range(10):
    t1 = time.time()
    generated_ids = model.generate(**inputs, max_new_tokens=128)
    t2 = time.time()
    print(f"{i}: {t2-t1}")
    generated_ids_trimmed = [
        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )
    print(output_text)
